This study develops a predictive model for forecasting gold and total medal counts by country at the 2028 Los Angeles Summer Olympics. A Markov chain model is ultimately selected for its ability to capture long-term trends in medal growth rates and its interpretability. The model incorporates host country effects and calculates 95% confidence intervals to quantify prediction uncertainty. Key findings indicate that the United States is expected to maintain a strong medal position, while China and Australia may see increases, and some countries may decline. Nations without prior gold medals are projected to achieve breakthroughs. A multiple linear regression model reveals a significant "great coach effect," contributing approximately 10% to medal counts, underscoring the value of investing in elite coaching. Additionally, a positive correlation is found between the number of events entered and medal outcomes. Model optimization via ridge regression achieves 83.76% accuracy in predicting athlete medal wins, with low mean squared error confirming reliability. These insights provide actionable guidance for National Olympic Committees in resource allocation, event selection, and coaching investment.

Zhang Yucheng, Liu Hao, Luo Shunan. Prediction of Olympic Medals and Analysis of Influencing Factors Based on Markov Chain Model and Ridge Regression Optimization Algorithm. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 3: 98-104. https://doi.org/10.25236/AJCIS.2026.090312.

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